Forward Collision Warning (FCW) generally refers to the use of one or more forward-looking sensors mounted on a host vehicle to detect obstacles in the host vehicle's path. If a potential collision danger to a sensed in-path target is determined to exist, the system can trigger a warning to help the driver avoid the potential collision. Alternatively or in addition, the FCW system may trigger an automatic braking intervention, and/or activate occupant safety systems before a collision actually occurs if it appears unavoidable.
FCW may be implemented along with adaptive cruise control (ACC) and/or collision mitigation by braking (CMbB) systems, all of which may utilize a common radar sensor. FCW may typically provide warning for moving and moveable vehicles, where moveable is defined as a vehicle that has previously been tracked by the radar as moving, but has come to a stop.
A camera-based computer vision system may be added to detect lane markings to thereby enable lane departure warning. The radar and the camera may be combined to detect and then classify targets as vehicles. Radar is the primary detection sensor, and computer vision is the vehicle classifier. Computer vision based vehicle classification is used to expand the operational scope of FCW to include stationary vehicles. Vehicle classification by camera also serves to reduce the likelihood that objects typically not in the roadway, e.g., trees, poles and overhead signs, trigger a false warning.
In systems without a computer vision camera, it would be advantageous to develop an FCW system capable of operating with radar only yet capable of accurately discrimination between stationary and moving targets. However, without a vision sensor for vehicle classification, non-vehicle objects such as trees, poles and overhead signs have the potential to be included as valid, in-path targets to which FCW may potentially respond by issuing a warning. If the object is not truly in the vehicle path, the resulting warning could be interpreted by the driver as a false warning.
One measure of system reliability is the number of false warnings for a given number of miles driven, or the number of false warnings for a given test route. As the number of false warnings increases, the system reliability decreases. If the system reliability is too low, some drivers may become habituated to ignore all FCW warnings or may turn off the FCW system altogether and lose the benefits of warnings for true potential collisions.
Stationary objects may be classified into three basic categories based on where the object is in relation to the roadway: on-road, overhead, and side-of-path or roadside objects. Roadside objects includes guardrails, roadside signs, trees, reflectors, concrete dividers, manhole covers, storm drain covers and raised lane edge markers (such as Bott dots).
Roadside objects may, of course, be present on both straight and curved roads, however false FCW warnings triggered by roadside objects are more prevalent in curved road sections. This is because on a straight section of road it is relatively easy to estimate the predicted path of the host vehicle, while when travelling on a curve (or about to enter a curve) the path prediction is more difficult.
An object is considered to be an out-of-path object only when the lateral clearance between the host vehicle and the target object, after road curvature is taken into consideration, is larger than half of the host vehicle width plus half of the target width. However, most radars currently considered to be appropriate for use on motor vehicles are not capable of accurately determining target width. Instead, the radar data may be used to estimate the target width, or all targets may be assumed to be of a standard width. An object with a smaller width, but laterally close to the host vehicle, could be treated as an in-path object and trigger an FCW warning.
Another factor that may cause out-of-path objects to be falsely identified as in-path is path prediction error. The lateral clearance between the host vehicle and the target object is calculated based on the predicted host vehicle path. A host vehicle path prediction algorithm is generally less accurate during steering transitions from straight to curved road or vice versa. These path errors and subsequent lateral offset errors can cause out-of-path objects on the side of the roadway to be considered as in the host vehicle's path.